R is a high-level statistical language and is widely used among statisticians and data miners to develop statistical applications. If you want to learn reward-based learning with R, then you should surely go for this Learning Path.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The highlights of this Learning Path are:
Beginning with the basics of R programming, this Learning Path provides step-by-step resources and time-saving methods to help you solve programming problems efficiently. You will be able to boost your productivity with the most popular R packages and data structures such as matrices, lists, and factors. You will be able to tackle issues with data input/output and will learn to work with strings and dates.
Moving ahead, you will know the differences in model-free and model-based approaches to reinforcement learning. This Learning Path discusses the characteristics, advantages and disadvantages, and typical examples of model-free and model-based approaches.You will learn Monte Carlo approach, Q-Learning approach, SARSA approach, and many more. Finally, you will take a look at model-free simulated annealing and more Q-Learning algorithms.
By the end of this Learning Path, you will be able to build actions, rewards, and punishments through these models in R for reinforcement learning.
About the Author
For this course, we have the best works of this esteemed authors:
In this video, we will see how to download and install RStudio, and set it up as an R editing environment.
In this video, we will Learn how to write, run, save and load R scripts in the RStudio source pane.
In this video, we will understand how to use numbers and perform arithmetic operations in R.
The aim of this video is to make us understand how to create and use R variables, and the basics of vectors and vectorised operations.
In this video, we will understand how to find and use functions.
In this video, we will see what data types are and how to work with vectors of different data types.
This video explains us what is the purpose and properties of matrices and arrays and how to create them, and how to subset elements from them.
The aim of this video is to make us understand what list data structure is and how does list differ from vectors.
In this video, we will understand how to use data frame as a flexible way to represent and work with tabular data in R.
This video explains us why factors exist and how to use them in base R.
Datasets are often provided to you in a delimited format such as CSV (comma-separated value). In this video, we will learn how to load data from this and other delimited formats into R.
When working with data, it’s often useful to subset a data frame by value. In this video, we will learn how to combine logical operators with data frame subsetting to subset datasets by value.
Large data sets can be difficult to understand at a glance. This video aims to explain how to apply a range of statistical summary functions to condense key statistical properties from dataset variables.
Although there are hundreds of statistical tests that can be performed in R, many of them are applied according to a similar pattern. In this video, we will learn how to perform three common statistical tests in two different ways.
Data sets will not always contain all the information you need. In this video, we will learn how to manipulate and combine variables to reshape a data set for your application.
When you finish working with a data frame, you need to write it back to file to work with it later or pass to somebody else. In this
video, we will learn how to write a data frame to file.
How do you represent the environment when you have no explicit MDP model?
How do you determine the optimal policy to “Solve” your reinforcement learning problem?
In this video, we will continue with the optimal policy to “Solve” your reinforcement learning problem.
How does one validate the model, as well as validate (and possibly update) the previously-determined optimal policy?
What are the state-value and state-action value functions?
How do MDP problem parameters affect the optimal policy solution?
How gamma affects policy improvement and optimal policy determination by diving deeper into the nature of the discount factor, gamma?
What is the nature of the Monte Carlo Model-Free approach to solving Reinforcement Learning problems?
What is the nature of the Model-Free Q-Learning approach to solve Reinforcement Learning problems?
Diving deeper into the nature of Q-Learning.
Explore the characteristics of the SARSA algorithm.
What is the nature of the Simulated Annealing algorithm alternative to Q-Learning?
How does one incorporate the discount factor into the previous Model-Free Q-Learning Reinforcement Learning algorithm?
How does one demonstrate the effects of Q-Learning algorithm control parameters using effective visualizations?
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